{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: UTF-8 -*-\n",
    "# Filename : BPTest.py\n",
    "\n",
    "from numpy import *\n",
    "import operator\n",
    "from bpNet import *\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据集\n",
    "bpnet = BPNet() \n",
    "bpnet.loadDataSet(\"testSet2.txt\")\n",
    "bpnet.dataMat = bpnet.normalize(bpnet.dataMat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制数据集散点图\n",
    "bpnet.drawClassScatter(plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-17.5777914   21.79176369  11.43868937  -8.66454747   4.91507001]]\n",
      "[[  5.4217694    5.15550801  -5.26855716]\n",
      " [  6.31792526  -3.86833903  -6.55511435]\n",
      " [ -8.26129016   8.90845848 -11.14640037]\n",
      " [  5.06219075 -15.46322504   7.09759052]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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UJMVuA86cOcMrr7xiOobVk2IXWV5KSgqATMVYOa01Z8+elWJPAyl2keUlJycDUuzW7vr16yQlJZGUlGQ6itWTYhdZXq5cuQC4efOm4STicfLly0eLFi0ICQkhICCA+/fvm45ktaTYRZaXLVs28ufPz+XLl01HEY+hlGLlypUMGDCA4OBgmjdvTmJioulYVskixa6UmquUuqyUOmyJ4wmR2YoUKcKlS5dMxxBP4OjoyOTJk/nqq6/YtGkTNWvW5PTp06ZjWR1LjdjnA40tdCwhMl3hwoVlxG5DevbsycaNGzlz5gzVq1dn/fr1Dy+CCwsVu9Y6CvjNEscSwgQZsdueBg0aEBsbS548eWjatCn58+fHy8uLkJAQTpw4gdbadERjMu0tRpRSfoAfIMuVhNWREbttKleuHAkJCURERLBp0ybCw8NZt24dACVLlqRhw4Y0atSIevXqkS9fPsNpM0+mXTzVWs/SWrtprd0KFSqUWacVIk2KFCnC9evXHy59FLbDyckJb29vZsyYwcmTJzl58iQzZsygSpUqLF26lFatWlGwYEFq1qzJqFGjuHHjhunIGU5WxQgBlC1bFoBp06YZTiLSq3Tp0vTu3Zs1a9Zw7do1duzYwccff0xiYiIjR45k7dq1piNmuKz7bq9C/E2rVq3w8fHhww8/pFy5cjRr1sx0JGEB2bJlw8PDAw8PD7Jnz87hw4epX7++6VgZzlLLHZcCMUBZpdQ5pVQ3SxxXiMzi4ODA/PnzcXV1pX379hw6dMh0JGFBWmuWLl1KrVq1ssSWv5ZaFdNea/2S1jqb1rqY1vprSxxXiMyUO3du1q5dS968eWnevDlXrlwxHUlYyOHDhzl27Bjt2rUzHSVTyBy7EH9TtGhR1q5dy8WLF2nZsqVcTLUTy5cvx8HBgVatWpmOkimk2IX4f9zd3Zk3bx47d+6kd+/eWXo9tD3QWrNs2TLq169P4cKFTcfJFFLsQjxCu3btGDFiBPPmzWPy5Mmm44h02LdvH6dOnaJt27amo2QaKXYh/kVgYCCtW7dmyJAhHD161HQc8Qzu3bvH2LFjyZYtGy1atDAdJ9NIsQvxLxwcHKhTpw5aa5RSpuOIp3Tz5k28vb0JDQ1l5MiR5M+f33SkTCPr2IV4jNDQUCpUqED58uVNRxFP4dKlSzRt2pT9+/czc+ZM/Pz8TEfKVDJiF+JfXL58maioKFq3bm06ingKP/zwAzVq1ODYsWOsXbs2y5U6yIhdiH+1Zs0aUlNTs8wSOXsQHR2Nl5cXjo6ObNu2DXd3d9ORjJARuxD/IjQ0lNdee41KlSqZjiLS4Ntvv6V+/foUKFCAmJiYLFvqIMUuxCNdu3aNrVu30qpVK7lwagOmTZtG69atcXFxITo6mtKlS5uOZJQUuxCPsGTJEu7fvy/z6zbgs88+w9/fH29vb7Zs2ULBggVNRzJOil2IB+7evcuyZcuoUaMG/v7+VK5cGVdXV9OxxGMsX76cwMBAfH19CQ0NJXfu3KYjWQUpdpHlXblyhaCgIEqWLEn79u25evUqU6dOZceOHTINY8Xi4+Pp3LkzHh4ezJo1C0dHR9ORrIasihFZVkJCAsHBwSxZsoTk5GQ8PT2ZNWsW77zzDg4OMuaxZhcvXsTb25vChQuzatUqsmfPbjqSVZFiF3YtNTWVixcvcvr0aU6fPs0vv/zC6dOnSUhIICYmhty5c9OlSxf69etHhQoVTMcVaZCcnEyLFi34/fff2bVrV5bZ2OtpSLELu/DTTz+xa9euh8X913/PnDlDSkrK/zy3QIEClCpVivHjx9O9e3deeOEFQ6nF09Ja07NnT2JjYwkNDcXFxcV0JKskxS5s2tGjRxkzZgxLly4lNTUVgBdffJESJUpQtWpVWrZsSYkSJShZsiQlSpSgRIkS5MmTx3Bq8aymTJnCggULCAwMlBvHHkOKXdikAwcOMHr0aL799lty587NwIED6dq1K6VKlSJnzpym44kMsGHDBoYMGULr1q0ZMWKE6ThWTYpd2AytNVu3bmXSpEls2LCB559/nmHDhtG/f39Zu2zHtNasWLECPz8/KleuzPz58+Xi9hNIsQurl5KSwvLly5k8eTIHDhygcOHCfP755/Tt25d8+fKZjicy0K+//kqfPn1Ys2YN7u7urFq1CicnJ9OxrJ4Uu7Ba169fZ+bMmUybNo3z589ToUIF5syZQ4cOHWS6xc5prVm4cCEBAQEkJSUxYcIEAgICeO45qay0kN8lkelu377NpUuXHvtx8eJFzp49S1JSEvXr12f27Nk0atRIfgTPAi5cuEC3bt3YuHEjHh4efP3117z++uumY9kUKXaRoa5cucKOHTuIiooiKiqKkydPkpiY+Mjn5suXjyJFivDiiy/yxhtv4OXlRadOnWRJWxYSHh5Ox44duXXrFtOmTaNPnz7yj/kzkGIXFnX+/PmHJR4VFfXwvUJz5sxJjRo16NKlC0WKFHlY4H99XrhwYXLkyGE4vTDl3r17BAYGMmbMGCpUqMCKFSvkhrF0kGIX6fLzzz+zfft2oqKi2L59Oz/99BMAefPmxcPDg06dOvH222/j5uYmt32LRzp//jzvvfceUVFRdO3alWnTpslmXukkxS6eyQ8//EBgYCDLli0DIH/+/Lz99tv07duXt99+mypVqsiFLvFY169fZ926dQwYMIA7d+6wcOFCOnXqZDqWXZC/eSLNEhMTWbFiBXPnziU6Oprs2bMzfPhw2rZtS4UKFWQuVDxWamoq+/btY+PGjWzcuJHY2Fju379PpUqVWLFiBeXKlTMd0W5YpNiVUo2BYMARmKO1HmuJ4wrztNbs2LGDuXPnsnLlSm7fvk25cuUYP348nTp14sUXXzQdUVipy5cvs3//fvbt20d8fDzbt2/n6tWrAFStWpWPPvqIxo0b8+abb8pPdxaW7t9NpZQjMB3wBM4Be5VSYVrro+k9tjDn7NmzLFy4kHnz5nHq1Cny5s1Lhw4d6NKlC2+++absUy4e0lpz7ty5hyX+18f58+cfPufVV1+lcePGNG7cGE9PT9mRMYNZ4p/JasBJrfVPAEqpZYA3IMVuY1JSUlizZg1z584lPDwcrTV16tRh5MiRtGzZUu74s3PXrl3j4sWLJCUlkZyc/D//fdTnv/32GwcOHGDfvn0PR+IODg6ULVuWOnXq4OrqiqurKy4uLnKHcCazRLEXBc7+7fE5oPr/f5JSyg/wA3jllVcscFphKT/++COzZ89m/vz5XLlyheLFizN8+HA6d+7Mq6++ajqeyCA3b95kx44dREREEBERwcGDB5/q+7Nly0bFihVp3rz5wxKvXLmyDACsgCWK/VE/k+t//ILWs4BZAG5ubv/4ushcycnJrFmzhpkzZxIZGYmjoyPe3t74+fnRoEEDeZsxO3T37l327t37sMhjY2O5e/cu2bNnx8PDg6CgIMqUKUOOHDnImTPnw4////ivX8uRI4dcMLdSlij2c0Dxvz0uBlywwHFFBjhx4sTD0fnVq1cpWbIkQUFBdOnShZdeesl0PGFBWmuOHj36sMi3b99OYmIiSilcXV0ZOHAgDRo0oGbNmuTKlct0XGFBlij2vcBrSqlSwHmgHfCeBY4rLOjMmTMEBASwevVqnnvuuf8Zncuoy7788ssvrFixgqVLl3LgwAEAXnvtNTp06ECDBg2oW7cu+fPnN5xSZKR0F7vW+p5Sqi+wiT+XO87VWh9JdzJhEcnJyUyfPp1PP/0UrTWBgYH07NlTlinambNnz7Jy5UqWL1/Onj17AKhWrRrTpk3Dy8uLEiVKGE4oMpNFFo9qrdcD6y1xLJF+SUlJhIeHs3LlSsLCwvjjjz9o2rQpISEhlCxZ0nQ8YSEXLlwgNDSU5cuXEx0dDYCrqyvjxo3Dx8eHUqVKGU4oTJG7AuxEUlISmzZteljmiYmJvPDCC7Rq1Yr27dvToEEDWXtuBy5fvvywzHfs2IHWmsqVKxMUFISPjw+vvfaa6YjCCkix27A7d+6wceNGQkND+e6770hMTCR//vz4+Pjg4+NDvXr1ZOMtO3H79m3GjRvH+PHjSUpKokKFCgQGBtKmTRu5FV/8gxS7jdFas2HDBhYtWsS6deu4efMmBQoUoG3btvj4+FC3bl2yZctmOqawEK01q1atYtCgQZw5c4Z27doxbNgwnJ2dTUcTVkyK3YacOXOGPn368P3331OwYEHat2+Pj48PderUkTK3Q0eOHKF///5s2bKFSpUqsW3bNmrXrm06lrABUuw24N69e0yfPp1hw4ahtWby5Mn07dtXytxO3blzh08++YRp06aRN29eQkJC6Nmzp2yUJdJM/qRYuc2bNzNgwACOHDlC48aN+fLLL2Vlix27evUqzZs3JzY2lh49ehAUFETBggVNxxI2RordSp08eZJBgwYRFhbGq6++yurVq/H29paVLXbs559/pnHjxpw+fZqVK1fSqlUr05GEjZJitzJ//PEHQUFBTJkyhRw5cjB27FgCAgLk/UDt3L59+2jSpAkpKSlERETg4eFhOpKwYVLsViI1NZX58+fzySefcOnSJbp06UJQUJDs35IFbNq0idatW5M/f34iIyMpX7686UjCxskmIVYgNjaWatWq0a1bN0qXLs3evXuZO3eulLqd01ozY8YMmjVrRunSpYmJiZFSFxYhxW7QH3/8Qd++fXnrrbe4ePEiS5YsYefOnbi5uZmOJjLY77//TuvWrfnggw/w9PQkKiqKl19+2XQsYSek2A1Zu3YtFSpUYMaMGfTr149jx47Rvn17uTiaBezatQsXFxfCwsKYMGEC69at4/nnnzcdS9gRmWPPZGfPnqV///6sXr2aypUr8+2331KtWjXTsUQmWbt2La1ataJEiRJER0fj7u5uOpKwQzJizyR3795lwoQJlCtXjo0bN/LFF18QFxcnpZ6FXLt2DT8/P6pUqcL+/ful1EWGkRF7JoiKiqJPnz4cOXIEb29vgoODZX/sLGjgwIH89ttvhIeHy9SLyFAyYs9Aly9fxtfXl9q1a3Pr1i3CwsJYs2aNlHoWtGHDBhYuXMjHH39MlSpVTMcRdk5pnfnvK+3m5qbj4uIy/byZJTU1lZkzZ/LJJ59w69YthgwZwrBhw8idO7fpaMKAxMREKlasSN68edm3b5/cbCaemVIqXmv9xGVzMhVjYUeOHKFHjx7ExMRQr149pk+fLvtlZ3EfffQR586dIzo6WkpdZAqZirGQpKQkPv30U9544w1OnDjBwoULiYiIkFLP4nbs2MGMGTMICAjgzTffNB1HZBEyFWMBUVFR+Pn58cMPP9CxY0cmT55MoUKFTMcSht28eRNXV1fu37/PwYMHcXJyMh1J2Li0TsXIiD0drl69SteuXalduzbJycls3LiRRYsWSakLjh49SrVq1Th16hSzZ8+WUheZSor9GWitmT9/PuXKlWPRokUMHTqUI0eO0KhRI9PRhBVYvHgx7u7uXLt2jc2bN1OvXj3TkUQWI8X+lI4dO0bdunXp0qULZcuWZf/+/YwdO1ZWvAi01gwfPpxOnTpRtWpV9u/fL6UujJBiT6M7d+4wYsQIqlSpwsGDB5k9ezY7duyQNxUWAKSkpODr60tQUBDdunVjy5YtsqmXMEaWO6bBli1b6NWrFydPnqRTp05MnDiRwoULm44lrMSNGzdo1aoVW7ZsYdSoUQwfPlw2cxNGSbE/xtWrVxk8eDALFiygTJkyREREUL9+fdOxhBU5f/48TZo04ejRo8yfPx9fX1/TkYSQYn8UrTXffPMNAwYM4Pr16wwbNoxhw4aRK1cu09GEFTl06BBNmjThxo0brF+/Hk9PT9ORhADSOceulPJRSh1RSqUqpezi3SFOnTpFo0aN6NSpE2XKlGH//v2MHj1aSl38j4SEBGrVqkVqaio7duyQUhdWJb0XTw8DLYEoC2QxKiUlhS+++AJnZ2diY2MJCQlh586dcnFU/MOvv/6Kl5cXefLkITo6Wjb1ElYnXVMxWutjgM1fKNq1axc9e/bkyJEjtGrViqlTp8qKBvFId+7cwdvbm2vXrrFz507ZqVNYpUxb7qiU8lNKxSml4q5cuZJZp32s33//nZ49e+Lh4UFiYiJhYWGEhoa5V23UAAAOB0lEQVRKqYtHSk1NxdfXl7i4OJYsWcIbb7xhOpIQj/TEYldKRSilDj/iw/tpTqS1nqW1dtNau5m+5V5rzfLlyylfvjxz5sxh4MCBHDlyBC8vL6O5hHULDAxk5cqVjB8/Hm/vp/rjL0SmeuJUjNa6QWYEySw//fQTffr0YdOmTVStWpX169fj6upqOpawct988w2ff/453bp1Y9CgQabjCPFYWebO07t37zJ27FgqVqzIrl27CA4OZvfu3VLq4omioqLo2rUrderUYcaMGTZ/TUnYv/Qud2yhlDoH1AC+V0ptskwsy4qJicHV1ZWPP/6Yd955h2PHjuHv74+jo6PpaMLKffnllzRo0ICSJUuyatUqsmfPbjqSEE+UrmLXWq/WWhfTWufQWhfRWlvV9oY3btzggw8+oGbNmly/fp01a9bw7bffUqxYMdPRhA1YsmQJffr0wdPTk5iYGPLnz286khBpYrd3nq5evZq+ffty8eJF/P39+fzzz8mbN6/pWMJG7Nu3j27dulGrVi1Wr14tI3VhU+xujv3cuXO8++67tGzZkkKFChEbG8t///tfKXWRZpcvX6ZFixYULFiQlStXSqkLm2M3xX7//n2mTZtGhQoVCA8PZ/z48ezduxd3d3fT0YQNuXv3Lm3atOHy5cusWbOGIkWKmI4kxFOzi6mYuLg4evXqRXx8PA0bNuSrr76iVKlSpmMJGzRw4EC2b9/O4sWLqVq1quk4QjwTmx6x37hxg379+lGtWjXOnz/PsmXL2Lhxo5S6eCZz584lJCSEQYMG0aFDB9NxhHhmNlnsWmuWLVtGuXLlmD59On379uX48eO0bdtW1hiLZxIbG0vv3r3x9PRk7NixpuMIkS42NxXz008/0bt3b8LDw6latSrfffcdbm52sWOwMOTQoUO0aNGCYsWKsWzZMp57zub+WgjxP2xmxH7v3j0mTpyIs7Mz0dHRTJ06ld27d0upi3SJiIjAw8MDR0dHvvvuO1mrLuyCTRR7fHw81apVY8iQIXh6enL06FH69esnd46KdJk/fz7vvPMOJUqUIDY2lgoVKpiOJIRFWHWx37x5k8GDB1OtWjV+/fVXQkNDWbNmDcWLFzcdTdgwrTWfffYZXbp0oU6dOuzYsUPuRhZ2xSonE7XWrFq1igEDBnDu3Dn8/PwYN24c+fLlMx1N2Lhbt27RtWtXVqxYQefOnZk1axbZsmUzHUsIi7K6EfuPP/5I48aN8fHxoUCBAuzatYuZM2dKqYt0+/nnn3nrrbcIDQ1l3LhxzJ07V0pd2CWrGbHfvn2bL774gvHjx5MzZ06Cg4Pp06ePrFAQFrFlyxbatGlDamoq69evp1Ejq9qvTgiLsooR+7p166hYsSKjR4/Gx8eH48eP4+/vL6UuLGLatGk0atSIl156ib1790qpC7tntNgvXrxImzZt8PLyIleuXERGRrJ48WJeeuklk7GEHYmMjMTf359mzZoRExNDmTJlTEcSIsMprXWmn9TNzU337t2bwYMHc/v2bUaMGMGHH34ou+gJi0pKSqJy5crcv3+fw4cPkytXLtORhEgXpVS81vqJN+8Ymes4ceIE3bt35+2332bWrFmULVvWRAxh58aMGcOPP/5IeHi4lLrIUoyM2HPkyKFDQkLo1q0bDg5WMc0v7MzRo0dxcXGhbdu2LFq0yHQcISwirSN2I8VetWpVHR8fn+nnFVlDamoqtWvX5ujRoxw/fpxChQqZjiSERVj1VIzswCgy0pw5c9i5cyfz5s2TUhdZksyDCLty8eJFPvzwQ+rWrYuvr6/pOEIYIcUu7MalS5do27YtSUlJfPXVV/KTociypNiFXYiMjKRKlSrs2bOHOXPm8Prrr5uOJIQxUuzC5oWGhtKoUSPy58/Pnj176Nixo+lIQhglxS5s2tdff03btm2pVq0a0dHRVKpUyXQkIYyTYhc2a9KkSXTv3h1PT082bdokO4AK8YAUu7A5WmuGDx/O4MGD8fHxISwsDCcnJ9OxhLAa6Sp2pdQEpdRxpdRBpdRqpZQMmUSGSk1NpV+/fgQFBdG9e3eWLl0qewwJ8f+kd8S+GXDWWlcGTgAfpz+SEI927949fH19mT59OkOGDGHWrFnyvrdCPEK6il1rHa61vvfgYSwgbxwpMoTWGj8/PxYvXkxQUBDjxo2TdepC/AtLzrF3BTZY8HhCAH+W+pAhQ5g3bx4jR47kk08+kVIX4jGeuFeMUioCePERXxqmtV774DnDgHvAN485jh/gB/DKK688U1iRNY0dO5ZJkybRt29fRo4caTqOEFYv3bs7KqV8gV5Afa317bR8j5ubm46Li0vXeUXWMHPmTHr16kWHDh1YuHChbPMssrRM2d1RKdUYGArUTmupC5FWK1asoHfv3jRt2pR58+ZJqQuRRun9mxIC5AU2K6UOKKW+skAmIZg3bx4dO3akZs2arFixgmzZspmOJITNSNeIXWst7wwsLOrOnTv07duXuXPnUq9ePVatWkXu3LlNxxLCpsjPtsJq3L17F09PT+bOncuwYcMIDw+XbQKEeAZG3kFJiEcZNWoUu3btYtGiRbJDoxDpICN2YRWio6MZM2YMvr6+UupCpJMUuzAuMTGRTp068corrzB16lTTcYSweTIVI4wbMGAAv/zyC9u2beP55583HUcImycjdmHUmjVr+Prrrxk6dCi1atUyHUcIuyDFLoy5ePEiPXr0wNXVlcDAQNNxhLAbUuzCiPv379OtWzdu3rzJ4sWLZU91ISxIil1kuhs3btCsWTPWr1/PxIkTKV++vOlIQtgVuXgqMtWPP/6Il5cXp06d4quvvqJnz56mIwlhd6TYRaaJiIjAx8cHR0dHIiIiqF27tulIQtglmYoRGU5rTUhICI0bN6ZYsWLs3btXSl2IDCTFLjJUSkoKvXr1ol+/fjRt2pTo6GhKlSplOpYQdk2KXWSYq1ev0rBhQ2bNmsVHH33E6tWryZs3r+lYQtg9mWMXGeLIkSN4eXlx4cIF2dRLiEwmxS4s7ujRo9SoUQMnJye2b99O9erVTUcSIkuRYhcWlZKSQqdOnciRIwe7d++WNy4XwgApdmFRn3/+Ofv27WP16tVS6kIYIhdPhcXExsYyZswYOnfuzLvvvms6jhBZlhS7sIhbt27RqVMnihcvTnBwsOk4QmRpMhUjLGLIkCGcOnWKyMhI2VNdCMNkxC7SbePGjXz55ZcMHDhQ7igVwgpIsYt0uXTpEl27dsXZ2ZnRo0ebjiOEQIpdpMPu3bupUqUKv//+O4sWLSJnzpymIwkhkGIXzyg2NpaGDRvi5OTE7t27cXFxMR1JCPGAXDwVT+2vUi9cuDDbtm2jWLFipiMJIf5GRuziqfxV6kWKFJFSF8JKSbGLNPt7qUdGRkqpC2Gl0lXsSqnPlVIHlVIHlFLhSqmXLRVMWJeYmBgZqQthI9I7Yp+gta6stXYB1gGfWiCTsDIxMTE0atToYakXLVrUdCQhxGOkq9i11n/87aEToNMXR1ijL7/8UkpdCBuitE5fFyulgoD3gRtAXa31lX95nh/g9+ChM3A4XSe2bgWBq6ZDZCB7fn32/NpAXp+tK6u1fuLbkD2x2JVSEcCLj/jSMK312r8972Mgp9Z65BNPqlSc1trtSc+zVfL6bJc9vzaQ12fr0vr6nriOXWvdII3nXAJ8Dzyx2IUQQmSc9K6Kee1vD5sDx9MXRwghRHql987TsUqpskAqcBrolcbvm5XO81o7eX22y55fG8jrs3Vpen3pvngqhBDCusidp0IIYWek2IUQws4YK3Z73o5AKTVBKXX8wetbrZTKZzqTJSmlfJRSR5RSqUopu1lappRqrJT6QSl1Uin1kek8lqSUmquUuqyUssv7R5RSxZVSkUqpYw/+bPY3nclSlFI5lVJ7lFIJD17bZ0/8HlNz7Eqp5/+6c1Up5Q9U0Fqn9eKrVVNKNQS2aq3vKaXGAWithxqOZTFKqfL8ecF8JjBYax1nOFK6KaUcgROAJ3AO2Au011ofNRrMQpRSbwM3gYVaa2fTeSxNKfUS8JLWep9SKi8QD7xrD///lFIKcNJa31RKZQN2Av211rH/9j3GRuz2vB2B1jpca33vwcNYwK52zNJaH9Na/2A6h4VVA05qrX/SWqcAywBvw5ksRmsdBfxmOkdG0Vr/qrXe9+DzROAYYBf7X+g/3XzwMNuDj8f2pdE5dqVUkFLqLNAB+91ArCuwwXQI8URFgbN/e3wOOymGrEYpVRJ4A9htNonlKKUclVIHgMvAZq31Y19bhha7UipCKXX4ER/eAFrrYVrr4sA3QN+MzGJpT3ptD54zDLjHn6/PpqTl9dkZ9Yhfs5ufIrMKpVQeYBUQ8P9mBWya1vr+g110iwHVlFKPnU7L0LfGs+ftCJ702pRSvkAzoL62wZsFnuL/nb04BxT/2+NiwAVDWcQzeDD/vAr4Rmv9rek8GUFrfV0ptQ1ozGM2UjS5KsZutyNQSjUGhgLNtda3TecRabIXeE0pVUoplR1oB4QZziTS6MEFxq+BY1rryabzWJJSqtBfK+uUUrmABjyhL02uilkF/M92BFrr80bCWJhS6iSQA7j24Jdi7WXFD4BSqgUwDSgEXAcOaK0bmU2VfkqpJsB/AUdgrtY6yHAki1FKLQXq8Oe2tpeAkVrrr42GsiCllAewAzjEn50C8InWer25VJahlKoMLODPP5cOwAqt9ajHfo8NzhIIIYR4DLnzVAgh7IwUuxBC2BkpdiGEsDNS7EIIYWek2IUQws5IsQshhJ2RYhdCCDvzfw7sjM+wJJg8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BP神经网络进行数据分类\n",
    "bpnet.bpTrain()\n",
    "\n",
    "print (bpnet.out_wb)\n",
    "print (bpnet.hi_wb)\n",
    "\n",
    "# 计算和绘制分类线\n",
    "x,z = bpnet.BPClassfier(-3.0,3.0)\n",
    "bpnet.classfyLine(plt,x,z)\n",
    "plt.show()\n",
    "# 绘制误差曲线\n",
    "bpnet.TrendLine(plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
